Effects of fine particulate matter and its chemical constituents on influenza-like illness in Guangzhou, China.

Abstract

Background: Although the link between fine particulate matter (PM_2.5) and influenza-like illness (ILI) is well established, the effect of the chemical constituents of PM_2.5 on ILI remains unclear. This study aims to explore this effect in Guangzhou, China.

Methods: Daily data on ILI cases, PM_2.5 levels, and specific PM_2.5 constituents (black carbon [BC], chlorine [Cl^-], ammonia [NH₄⁺], nitrate [NO₃^-], and sulfate [SO₄^2-]) in Guangzhou, China, were collected for the period of 2014-2019. Additionally, data on gaseous pollutants and meteorological conditions were obtained. By using quasi-Poisson regression models, the association between exposure to PM_2.5 and its constituents and ILI risk was estimated. Stratified subgroup analyses were performed by gender, age, and season to explore in depth the effects of these factors on disease risk.

Results: Single-pollutant modeling results showed that an increase of one interquartile range (IQR) in Cl^-, SO₄^2-, PM_2.5, NH₄⁺, BC, and NO₃^- corresponded to relative risks of ILI of 1.046 (95 % CI: 1.004, 1.090) (lag03), 1.098 (95 % CI: 1.058, 1.139) (lag01), 1.091 (95 % CI: 1.054, 1.130) (lag02), 1.093 (95 % CI: 1.049, 1.138) (lag02), 1.111 (95 % CI: 1.074, 1.150) (lag03), and 1.103 (95 % CI: 1.061, 1.146) (lag03), respectively. Notably, the association between ILI and BC remained significant even after adjusting for PM_2.5 mass. Subgroup analyses indicated that individuals aged 5-14 and 15-24 years may exhibit higher sensitivity to BC and Cl^- exposure than other individuals. Furthermore, stronger associations were observed during the cold season than during the warm season.

Conclusions: Results showed that the mass and constituents of PM_2.5 were significantly correlated with ILI. Specifically, the carbonaceous fractions of PM_2.5 were found to have a pronounced effect on ILI. These findings underscore the importance of implementing effective measures to reduce the emission of specific sources of PM_2.5 constituents to mitigate the risk of ILI. Nevertheless, limitations such as potential exposure misclassification and regional constraints should be considered.